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Libby, David L.; Novick, Melvin R. – Journal of Educational Statistics, 1982
Two multivariate probability distributions, a generalized beta distribution and a generalized F distribution, are derived. Formulas for the moments of these distributions are given and an example of the bivariate generalized beta is presented. (Author/JKS)
Descriptors: Hypothesis Testing, Multivariate Analysis, Statistical Distributions
Peer reviewed Peer reviewed
Lutz, J. Gary; Cundari, Leigh A. – Journal of Educational Statistics, 1989
Means of identifying sources of rejection of hypotheses regarding linear multivariate statistical models are discussed. Problems with the use of a global test using Roy's largest root criterion and means of solving them are presented, along with a practical application of the techniques. (TJH)
Descriptors: Hypothesis Testing, Mathematical Formulas, Mathematical Models, Multivariate Analysis
Peer reviewed Peer reviewed
Szatrowski, Ted – Journal of Educational Statistics, 1982
Known results for testing and estimation problems for patterned means and covariance matrices with explicit linear maximum likelihood estimates are applied to the block compound symmetry problem. An example involving educational testing is provided. (Author/JKS)
Descriptors: Hypothesis Testing, Mathematical Models, Maximum Likelihood Statistics, Multivariate Analysis
Peer reviewed Peer reviewed
Betz, M. Austin; Elliott, Steven D. – Journal of Educational Statistics, 1984
The method of unweighted means in the multivariate analysis of variance with unequal sample sizes was investigated. By approximating the distribution of the hypothesis sums-of-squares-and-cross-products with a Wishart distribution, multivariate test statistics were derived. Monte Carlo methods and a numerical example illustrate the technique.…
Descriptors: Analysis of Variance, Estimation (Mathematics), Hypothesis Testing, Multivariate Analysis
Peer reviewed Peer reviewed
Rosenbaum, Paul R. – Journal of Educational Statistics, 1986
Using data from the High School and Beyond, this article presents statistical procedures to estimate the effect of dropping out of high school on cognitive achievement test scores. Each sampled dropout is matched to a student remaining in the same school. Methods for addressing the possible omission of covariates are described. (BS)
Descriptors: Achievement Tests, Analysis of Covariance, Dropouts, Effect Size